code: def __init__(self, layers, in_channels=3, gcb_config=None):
      assert len(layers) >= 4
  
      super(TableResNetExtra, self).__init__()
      self.inplanes = 128
      self.conv1 = nn.Conv2D(
          in_channels,
          64,
          kernel_size=3,
          stride=1,
          padding=1,
          bias_attr=False,
      )
      self.bn1 = nn.BatchNorm2D(64)
      self.relu1 = nn.ReLU()
  
      self.conv2 = nn.Conv2D(
          64, 128, kernel_size=3, stride=1, padding=1, bias_attr=False
      )
      self.bn2 = nn.BatchNorm2D(128)
      self.relu2 = nn.ReLU()
  
      self.maxpool1 = nn.MaxPool2D(kernel_size=2, stride=2)
  
      self.layer1 = self._make_layer(
          BasicBlock,
          256,
          layers[0],
          stride=1,
          gcb_config=get_gcb_config(gcb_config, 0),
      )
  
      self.conv3 = nn.Conv2D(
          256, 256, kernel_size=3, stride=1, padding=1, bias_attr=False
      )
      self.bn3 = nn.BatchNorm2D(256)
      self.relu3 = nn.ReLU()
  
      self.maxpool2 = nn.MaxPool2D(kernel_size=2, stride=2)
  
      self.layer2 = self._make_layer(
          BasicBlock,
          256,
          layers[1],
          stride=1,
          gcb_config=get_gcb_config(gcb_config, 1),
      )
  
      self.conv4 = nn.Conv2D(
          256, 256, kernel_size=3, stride=1, padding=1, bias_attr=False
      )
      self.bn4 = nn.BatchNorm2D(256)
      self.relu4 = nn.ReLU()
  
      self.maxpool3 = nn.MaxPool2D(kernel_size=2, stride=2)
  
      self.layer3 = self._make_layer(
          BasicBlock,
          512,
          layers[2],
          stride=1,
          gcb_config=get_gcb_config(gcb_config, 2),
      )
  
      self.conv5 = nn.Conv2D(
          512, 512, kernel_size=3, stride=1, padding=1, bias_attr=False
      )
      self.bn5 = nn.BatchNorm2D(512)
      self.relu5 = nn.ReLU()
  
      self.layer4 = self._make_layer(
          BasicBlock,
          512,
          layers[3],
          stride=1,
          gcb_config=get_gcb_config(gcb_config, 3),
      )
  
      self.conv6 = nn.Conv2D(
          512, 512, kernel_size=3, stride=1, padding=1, bias_attr=False
      )
      self.bn6 = nn.BatchNorm2D(512)
      self.relu6 = nn.ReLU()
  
      self.out_channels = [256, 256, 512]
  
file_path: PaddleOCR/ppocr/modeling/backbones/table_master_resnet.py
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
label_desc: Use image node_id 1 to create a new TableResNetExtra object from inherited base classes: nn with example: obj = TableResNetExtra(layers, in_channels, gcb_config)